How does overfitting relate to variance?

Updated May 15, 2026

Short answer

Overfitting occurs when a model has high variance and learns noise in training data instead of general patterns.

Deep explanation

Overfitting happens when a model becomes too complex and adapts too closely to training data, including noise and outliers. This leads to excellent training performance but poor generalization to unseen data, which is a hallmark of high variance.

Real-world example

A deep decision tree perfectly classifies training customers but fails on new users.

Common mistakes

Thinking overfitting is just a data problem, not model complexity issue.

Follow-up questions

How do you reduce overfitting?
Is overfitting always caused by small datasets?

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